We implement our method in PyTorch [1]}.
Our model is trained from scratch using the Adam optimizer [2]} (\(\beta _1 = 0.9\) , \(\beta _2 = 0.999\) ), with a learning rate of \(10^{-4}\) .
For DDFF 12-Scene [3]}, 4D Light Field Dataset [4]}, FlyingThings3D [5]}, and the DefocusNet dataset [6]}, we use 10, 10, 15, and 5 as the input stack sizes, respectively.
In unsupervised learning, the weight of smoothness loss \(\alpha \)  is set to \(0.002\)  in all experiments.
We apply random spatial transformations (flipping, cropping, rotation) and random color jittering (brightness, contrast, and gamma) for data augmentation during training.
The training patch size is \(256 \times 256\)  after random cropping except for DDFF 12-Scene [3]}.
Because the image size of DDFF 12-Scene is \(224 \times 224\) , which is larger than \(256 \times 256\) , we do not apply random cropping on this dataset.
All the experiments are conducted with a single NVIDIA GTX 1080 GPU.
